Numpy MaskedArray.any() function | Python

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries.

numpy.MaskedArray.any() function returns True if any of the elements of masked array evaluate to True.Masked values are considered as False during computation.

Syntax : numpy.MaskedArray.any(axis=None, out=None, keepdims)



Parameters:
axis : [None or int or tuple of ints, optional] Axis or axes along which a logical AND reduction is performed.
out : [ndarray, optional] A location into which the result is stored.
  -> If provided, it must have a shape that the inputs broadcast to.
  -> If not provided or None, a freshly-allocated array is returned.
keepdims : [ bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return : [bool or ndarray]A new boolean or ndarray is returned unless out is specified, in which case a reference to out is returned.

Code #1 :

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# Python program explaining
# numpy.MaskedArray.any() method 
  
# importing numpy as geek 
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
  
# creating input array 
in_arr = geek.array([1, 2, 3, -1, 5])
print ("Input array : ", in_arr)
  
# Now we are creating a masked array
# by making third entry as invalid. 
mask_arr = ma.masked_array(in_arr, mask =[0, 0, 1, 0, 0])
print ("Masked array : ", mask_arr)
  
# applying MaskedArray.anom methods to mask array
out_arr = mask_arr.anom()
print ("Output anomalies array : ", out_arr)

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Output:

Input array :  [ 1  2  3 -1  5]
Masked array :  [1 2 -- -1 5]
Output  array :  True

 

Code #2 :

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# Python program explaining
# numpy.MaskedArray.any() method 
  
# importing numpy as geek 
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
  
# creating input array 
  
in_arr = geek.array([1, 20, 30, 40, 50])
print ("Input array : ", in_arr)
  
# Now we are creating a masked array by making 
# all entry as invalid. 
mask_arr = ma.masked_array(in_arr, mask ='True')
print ("Masked array : ", mask_arr)
  
# applying MaskedArray.any methods to mask array
out_arr = mask_arr.any()
print ("Output array : ", out_arr)

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Output:

Input array :  [ 1 20 30 40 50]
Masked array :  [-- -- -- -- --]
Output array :  --


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